{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qual = 'BC?BABABBA@BCBAC>A<4+?BA><B=@?AB@B@A>?BB=B.?7?>1;<??=@A8?8=B8B>?B@46==8863<'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phred = [ord(b)-33 for b in qual]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[33,\n",
       " 34,\n",
       " 30,\n",
       " 33,\n",
       " 32,\n",
       " 33,\n",
       " 32,\n",
       " 33,\n",
       " 33,\n",
       " 32,\n",
       " 31,\n",
       " 33,\n",
       " 34,\n",
       " 33,\n",
       " 32,\n",
       " 34,\n",
       " 29,\n",
       " 32,\n",
       " 27,\n",
       " 19,\n",
       " 10,\n",
       " 30,\n",
       " 33,\n",
       " 32,\n",
       " 29,\n",
       " 27,\n",
       " 33,\n",
       " 28,\n",
       " 31,\n",
       " 30,\n",
       " 32,\n",
       " 33,\n",
       " 31,\n",
       " 33,\n",
       " 31,\n",
       " 32,\n",
       " 29,\n",
       " 30,\n",
       " 33,\n",
       " 33,\n",
       " 28,\n",
       " 33,\n",
       " 13,\n",
       " 30,\n",
       " 22,\n",
       " 30,\n",
       " 29,\n",
       " 16,\n",
       " 26,\n",
       " 27,\n",
       " 30,\n",
       " 30,\n",
       " 28,\n",
       " 31,\n",
       " 32,\n",
       " 23,\n",
       " 30,\n",
       " 23,\n",
       " 28,\n",
       " 33,\n",
       " 23,\n",
       " 33,\n",
       " 29,\n",
       " 30,\n",
       " 33,\n",
       " 31,\n",
       " 19,\n",
       " 21,\n",
       " 28,\n",
       " 28,\n",
       " 23,\n",
       " 23,\n",
       " 21,\n",
       " 18,\n",
       " 27]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pr_phred = [10**(-q/10) for q in phred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0005011872336272725,\n",
       " 0.00039810717055349735,\n",
       " 0.001,\n",
       " 0.0005011872336272725,\n",
       " 0.000630957344480193,\n",
       " 0.0005011872336272725,\n",
       " 0.000630957344480193,\n",
       " 0.0005011872336272725,\n",
       " 0.0005011872336272725,\n",
       " 0.000630957344480193,\n",
       " 0.0007943282347242813,\n",
       " 0.0005011872336272725,\n",
       " 0.00039810717055349735,\n",
       " 0.0005011872336272725,\n",
       " 0.000630957344480193,\n",
       " 0.00039810717055349735,\n",
       " 0.0012589254117941675,\n",
       " 0.000630957344480193,\n",
       " 0.001995262314968879,\n",
       " 0.012589254117941675,\n",
       " 0.1,\n",
       " 0.001,\n",
       " 0.0005011872336272725,\n",
       " 0.000630957344480193,\n",
       " 0.0012589254117941675,\n",
       " 0.001995262314968879,\n",
       " 0.0005011872336272725,\n",
       " 0.001584893192461114,\n",
       " 0.0007943282347242813,\n",
       " 0.001,\n",
       " 0.000630957344480193,\n",
       " 0.0005011872336272725,\n",
       " 0.0007943282347242813,\n",
       " 0.0005011872336272725,\n",
       " 0.0007943282347242813,\n",
       " 0.000630957344480193,\n",
       " 0.0012589254117941675,\n",
       " 0.001,\n",
       " 0.0005011872336272725,\n",
       " 0.0005011872336272725,\n",
       " 0.001584893192461114,\n",
       " 0.0005011872336272725,\n",
       " 0.05011872336272722,\n",
       " 0.001,\n",
       " 0.00630957344480193,\n",
       " 0.001,\n",
       " 0.0012589254117941675,\n",
       " 0.025118864315095794,\n",
       " 0.0025118864315095794,\n",
       " 0.001995262314968879,\n",
       " 0.001,\n",
       " 0.001,\n",
       " 0.001584893192461114,\n",
       " 0.0007943282347242813,\n",
       " 0.000630957344480193,\n",
       " 0.005011872336272725,\n",
       " 0.001,\n",
       " 0.005011872336272725,\n",
       " 0.001584893192461114,\n",
       " 0.0005011872336272725,\n",
       " 0.005011872336272725,\n",
       " 0.0005011872336272725,\n",
       " 0.0012589254117941675,\n",
       " 0.001,\n",
       " 0.0005011872336272725,\n",
       " 0.0007943282347242813,\n",
       " 0.012589254117941675,\n",
       " 0.007943282347242814,\n",
       " 0.001584893192461114,\n",
       " 0.001584893192461114,\n",
       " 0.005011872336272725,\n",
       " 0.005011872336272725,\n",
       " 0.007943282347242814,\n",
       " 0.015848931924611134,\n",
       " 0.001995262314968879]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_phred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114b9d160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ueg7wALcqpW4BKrTWP1BKfQJYjfH/36e1bs5dcoWxktxB6/DZy0pVJ4VMoi5iL+SaUcVe\nax0DPpSyebtj/6PAoyOcf+l4EyecOOmWJAQZenmyyCTqEuZYyDUyqarAyezGcU6qErHPF5ncOBIy\nQcg1IvYFjnN2rC+Dz17G2eePTBa8TKwSco2IfYGTbNk7YuNIPPuTQmY3joi9kFtE7AucbGbQSmyc\n/JFJ1MWyF3KNiH2B4wyE5uyg9UsH7Ukhk6hLB62Qa0TsC5xsOmhl6GX+EDeOcLIQsS9wMrlxkoZe\nykpVeSOjG0fEXsgxIvYFTsZJVT6JZ38yyOTGEZ+9kGtE7Ascp4vGn2nopfjs84bTsvdk2C4IuUDE\nvsDJ6MaRcAknBacFX1kWGPxbxF7INSL2BU7YES4hkGE0TjQak8Uz8oTTN19dUZx2uyDkAhH7Aiej\nz94h/HEgJmKfF5xDLKsrioa2i89eyDEi9gVO5klVnpTjROzzgdONU10+JPay6LiQa0TsC5xwZPQO\nWpBO2nzh9M1XlzvcOGLZCzlGxL7AyWjZ+8SyPxkki31R0nbpNxFyiYh9geMU+4DfGQgtOeslzHF+\nSO6gHRJ7WXRcyDUi9gVO9pa9CE2uicfjGX32ICNyhNwiYl/gZJpU5fV4kib1iBsn90SicaKOtQOq\nUsR+IkbkhMJRmo8d50h7n4ywEpLIZg1a4Q1MUiA0x3BLj8eDz+cdtOjFss89qZ2w5aUB/D7PYEN7\nIpZ9b3+Yh9fu4dnNLYP9Ag3VJbxp+QzedM50vCmjrwT3IWJf4CStQZvipzdCY/6OympVOSc1jHFJ\nwEdxwEckGkm7P1t6+kJ85ecbaGnrS9p+rGuAB558nb2Hu7nt2tPxekTw3Yy4cQqYWDzZbZDqp5f4\nOPnFabl7POZLq6TIn3b/WPjVU7uGCb2TdVuP8OK2o+O6tlA4iNgXMKkjbFIte5/EtM8rTp98SZEP\nj8dDSZFvaP84xP74QJgXXjsy6nFrNhwc87WFwkLEvoBJFfBhbhxvcnwcIbc4LffigBH5YofYj2di\nVXPr8aT4R5nY3dIz5msLhYWIfQETThFwZwctyGpV+cYp9gn3TUL0YXyWfbZueOmfFUTsC5hIJNWN\nIz77k4nTjZOw6JPdOGPvoJ3ZVJl0jUycNrNmzNcWCgsR+wImVcBH9NnL0oQ5J8myDwwX+/G4cYqL\nfFy8ZOqox73pnBljvrZQWIjYFzDhFNeMz5vZspelCXOP002TsOyLHaNxxruAyU2r5jJ/enXG/Vev\nmMmSefXjurZQOIjYFzCpY+w9KQ5ef9JqVWLZ55pgymgcGLLwYfxDL4uLfHzq5rN483nJ1rvP6+HD\nNyzmHZfNH9d1hcJCxL6AcbpmnEHQEviSfPZi2ecap09+okbjJAj4fZw5N9l6j8biLJ3fMO5rCoWF\niH0BM9Ls2dRtMvQy9wTTuXFOcDSOk67eYFbbBHcyargEpZQXuAdYCgSB27TWOx37rwM+D0SA+7TW\n9yqlAsB9wGygGPii1vqRiU++MBKZgqANbXN20Ipln2tSJ1U5/4WJEPvQsG2dvSEaakpP6LpCYZCN\nZX8DUKK1vgD4NPD1xA4r6ncDbwYuAW5XSk0C/hpo01pfDLwF+M5EJ1wYnaQgaKNY9uKzzz3pJlUl\njcY5QbHvSGPFp9smuJNsxP4i4HEArfU6YLlj3yJgp9a6Q2sdAtYCq4BfAZ+zx3gwVr+QZ5LcOP5R\nLHsR+5wzkG5SldOyP8EQx2kt+x4Re8GQTdTLKqDL8TuqlPJrrSNp9vUA1VrrXgClVCXwIPAvo92k\ntrYMv3/0ySFC9pQeGMqakiI/jY2VSfsrHGugFhUFhu0XJhanp6yhrpzGxkomdQ4MbgtHYieUB31p\nvgyC0bjkqwBkJ/bdgLO0eK3Qp9tXCXQCKKVmAA8B92it7x/tJh0dmaP2CeOjveO441ec1tbk+Chh\nhyXZ3TswbL8wsfT2DVne4WCY1tYeBhzb+u228dKapg4dau2RfC1wsm3MsxH7Z4HrgF8qpc4HNjv2\nbQMWKKXqgF6MC+cu67f/I/ARrfWTY0m4MHFER+ugdYyzl0lVuSfZjTN86GVi0fHU+RDZIm6cwiIW\nj7Ntbwd7D3fj9Xo4bUYNc6dUjbt8ZCP2DwFXKqWew/jfb1VK3QJUaK1/oJT6BLAa4/+/T2vdrJT6\nJlALfE4plfDdX6217h9XKoVxkdxBO7yASAdtfgmmiY1T6hD7xKLjzuGY2dIfjKQdp9+RpgEQTn12\nH+rm3t+9xpH25K+1OVOq+Lu3nU5TbdmYrzmq2GutY8CHUjZvd+x/FHg05Zx/Av5pzKkRJpRMi40n\nkHj2+WVglBDHYEbkjEfsu46nF/XOnuAJfS0I+edgay9fe2Bj2tFZe1q6+er9G/n83y6nuqI4zdmZ\nkUlVBczoo3Eck6okEFpOiURjSY1vSZpJVTD+ETnOyVNOXQ+Goyc8fl/ILw89s3vEYbgdPUFWrz8w\n5uuK2Bcw4bFMqhLLPqeEUkQ8EQDN7/Mm5cN4x9p3Otw1k+vKcNrxHeK3f8PQ0xdi085jox63dnML\n8fjY6qyIfQGT7MZJExvHKz77fJFqXTsDoCWHTBjflJROh2VfV1lMZXlR2n3CqU1HT5BsNLy3P0wo\nPLY6K2JfwPQNhAf/9qbx2TobAImNk1ucYu8BAoGhqjcRi447R+JUVxRT6/DnimV/YrR29vPa3nb2\ntHQTy3FYkbLibMbMmLqbuvLcqOeMJ0HCqU179wC/XLOTF7cdHdz2/NbDVJQFuOGiuYOFxC9RL/OG\nc6RMUZEvqfGdiPg4nceHBL26oojj/WH22XXIxbIfH3sPd/PLP+9k+/7OwW31VcW8ZcUsLj97Wk46\nveurS5g1uZJ9h0eeG3H2aY14x7jWpFj2BUZ79wBf/tnLrN92FKd8R6JxHlu3n2//+tVBl42sVJU/\nBtKsUpVgIsIcOy37mopiaiuHLPvOHhl+OVZ2NnfxlZ9vSBJ6gLbuID9/Yge/WrMrJ/f1eDxce8Hs\nEY/xeT1cvWLWmK8tYl9g/OqpXbR3Z7bktuxp57kthwGx7PNJuvDGg78nIMyx03qvqSimxuHGEct+\nbMTjcX78h20j+sQfX7+fPS3dObn/OaqRd1+RfsEZv8/D39+wmFmTxx4CQ8S+gOjpC/HS9qOjHrdm\nYzMg8ezzyUB4qOM11bI/0UXHIcVnX15EjcOyl8iXY2PHgU5a2kYP3/L0puacpWHx3PTLSL77igWc\nfVrjuK4pPvsCoqWtj2gWHUgHj/YCqcsSimWfS4JpQiWk+z0eN04oHKUvONRI1FQUJX0hiGU/Nvbb\n+jEaB7I8bjy8srMt7fYTabhF7AuIdGPp05Hw1fskXELeSHbjJFe7E110PHX2bHVFMUGHC6KrN0Qs\nHk87IksYTrq1H9KRbX0bD6/uGhprH/B7CdsJks2txzOdMirixikgZjRVUFEaGPW4M2bXASlDL2Wl\nqpwykCYuToITXXTc6cIpLfZRHPBRUzE0zj4ai9PTF053qpCGRbNrszrudFuPJpq+gTA7HOHJLzpz\nyuDfh46J2AsYC+Dys6eNetyVy2cAEggtnwRzOBrH6aaptmsUVJQGkhpziX6ZPZNqyzhrlIXaA34P\nq5ZOzcn9t+xpJ2ZnVpUV+5Puc7Sjf9hs7GwRsS8wrl05e8SC+o7L5rFwlrFcJBBa/hjJsj/R0TjJ\nI3GMRe/xeJJG5Egn7dh43zULmd5YnnH/4rn1bN7dxp6W7jGHLchEMBxlw45W/vjiUNybxXPrmNpQ\nPuiCi0NWncfpEJ99geH3efnITWdy573rONrRb7d5WDqvgTctn46aOfSJGhDLPm9k20F7oj57ZyTE\nmspijnWZlbDEsh8bVWVF3Pnec7jje8/T0z/cBbZxxzE27jB+9VmTK3nfWxaOazgkmKGeq9cf4PfP\n7+X4QPJorCK/j4Dfy6S60kGRP3Ts+LjuJWJfgMTi8aSx9p+8eRmnzagZdpx00OaPdIuNJzjRRceT\n3ThDvnoZa39ixOMkCX1dVXHaOSz7Dvfw1fs3cOdfn8P0poox3+ehv+zhd8/tTbtv7eYWpjeWM62h\nfFDsm8fptxc3TgGy/0jv0CxZryejFeD06cbj5Dzuh5sZ0Y2TZtHxeDzOvsM9bNndxoGjvSO6ClJn\nzw79PST8Eh9n7Ow/MhSywOf1jDhZcSAU5Rdrdo75Hq2d/fw+g9AnePDp3TRUlwz+Hm8nrVj2Bciu\n5qGe/OlNFRkXw/B7k9v6SDRGkVcWfc8FI3XQJo/GifDi9qP89i+7k3yz0xvLuemSeWn7YzqTxH5I\n4JNCJsiKVWNm7+FksR9txNrWPe0c6+qnobo063s8u7mF0UysSDSWNJrqYOv4xveLZV+A7Do0JPbz\np1ZnPC417LF00uYO58zY4Zb9kM11vD/C9367ZVgn3MHW43z7wVd5fuvhYdfuSgqC5rTsJfLlibDP\nYdmHs3RztnaMbeXVI1ke73SzHusaGJe7Tyz7AmRX81DMjrnTqjIe50uZFCLB0HJHusXGEzjFfyRR\niQP/u1pz1vwGSm0o3FSrL8myP4k++2gsxsYdx3huy2E6eoJUlPo5d9EkVpw+afBLMxKN8eL2o6zb\neoSu40Gqy4s5//RJLF/YNGL43v1HelizsZn9R3rwej0snFnLpWdNo97h6pgInJEni/y+rIbFpk6Y\nA/OcG3a08tyWw3T2BqkqK0LNrKGzN8hre9uzSktNZXHS18WhtuPMmZK5bqdDxL7A6OwN0tY9MPh7\n3rTsLfuoWPY5I2mx8RQ3TmlR9q6zYCjKC68d4dJlZj5Fd+rs2fLk0TgJevvDhCOxMcdAHw99A2G+\n+eCrvH6wK2n71r0dPLZuH59411kUBXzc/YtNKaEJetm8u43H1+/nE+9cOmyN1Xg8zm//sodHU3zc\nu5q7Wb3+AB+87nTOXdg0Ic/QH4xw2PF1tWh2DZteTx/CIEFtZTGzJid30Pb0hfjGr15hT0tyyOIt\ne7IT+QTnnNbElj3tgzNoDx0bu9iLG6fAcFr1lWUBGkewdlKne8uInNyRPPQyNVzC2PpJnD5b57DL\nIr+X0uKhazmtfEhepzaX3Pvoa8OEPsGRjn7u/uUmvvPrVzPGoDlwtJfv/GbzsE7ptZtbhgl9gkg0\nxg8e2TpqHPhsOXC0d9CXHvB7eduFc/GNEj/+qnNnJK3+Fo/H+f7DW4cJ/VhZML2aedOqmNYwNO5/\nPGETROwLjN0Of/28qdUjLrCQWnhF7HNDLBYnFBm+2HiCTB3omXA20knDLiuKkvK7pMifJP75mFh1\n8Ggvr+wa2QI+3N7PrkMjhwfedagb7YglH4vH+cPz+0Y8JxqLs/rF/dkndgScjcaMpgpmT67k7952\nRtrlPcGEJb7y3BlJ2/a09LBtX8cJpWN6YwUfvvFMPB4PU51iP44ROeLGKTCcI3HmjeCvBzPL0ukH\nFDdObkj19aZa8olFx7PtIHfGZOnsTT+hKkFNRTH9QeOOyEcn7cbXWyfsWht2tA7O9m5p68uqM3PD\njlbi8fgJryLlHImTGLq8fGETsyZX8tTGZrbubaelrW8wQFlVedGwe27YMf53cdqMai5eMpXzFjUR\n8JvyMq1hyEV06NjYR+SIZV9AhMJR9jgK6bwRRuIkSIqPcwIdtPF4nGAoKmP105A6KzadJZ+tdT+1\noZzFc4fE3umaqSlPdtvsaenmuGNS0P88tp3fPLOLnr4QwXA0J19y/eNcfCX9tYZGMA0Es4vzHwrH\nslqwezScI3FmTxqap9JYU8o7LpvPv916HjdfPrTAyPrXjgwKf4Lxrk0A8MFrz+DCM6cMCj3ANEf4\nhrbuYNIa09kgln0BcPBoL4+9sI8Xtx9Nsg6dHXSZ8Ps8BG2ZGc/Qy+6+EKvX72ftqy309IXxeT0s\nO62Rq1fMHNaB1NJ23I7MCDC9qaJgQ+4eae+jrXuA8pIAMyZVDLfs0wh7SZF/2FT5VHxeD/9w4+Kk\n95bJsn9x+1F+8MjWpLHhA6Eov3tuH394fj+xeByPx0RAveq8mZwxZ+QIjuFIlH1HeglHYkypL0sa\n1umkqSb7Meaj4fV46A9GKC3201BdggdGHZNeX1Uy5rVZUwmGorS0DblJMk1KPHfRJO7/0+tEY3GO\nD0T4w7pA7a/xAAAfd0lEQVS9nDW/kRmTKsYdrAxM+ahKabjBNHgeD4ON2SfveY5VS6fy0ZvPzuq6\nbyixj8XitHUP4AHqJiBT3yjsaenmhdeO0N0Xorq8iAvOmMxMa21s3t3Gd36zeZhVAfCVn2/gjluW\nMaU+c0Anp98+Ehm9gEaiMTa9fowte9rp7QuzfX9H0sIZ0Vicl7YfZeOOVm5/2xmcu7CJ7fs6ePDp\nXex2+Gkn1ZXxtgtnc8EZk5Ou3zcQprsvTGVZgPKS0cM1TxQdPUHWbm7hcNtxigI+lsyrZ8ncerr7\nwoQiUeoqi5OsrHTsONDJg0/tYqfDldZUU8r5Z0wa/F0U8KYtt5k6aZ3unWgszt7DPQT8Xmori9nV\n3J00dC/xkdbePcC9j76WcRJQIqJiPG5GhWzZ0851K2ezcvFkaiuLKQqYYYadPUECfi/PvHKIP29o\nptd+JXg8cNb8Bt51xYJh4n7eoiYe+PPrIy7pl7jGaBb4X15tYf22o6w8czJ/dck8ls5vYNPOYyOe\nc/FSEw6463iI5za3cLD1OAG/hzPm1LNsQQPH+8MMhKLUVBZn/JoyM5bN336fN8lX7qSiNMC8adXs\nOGD6Fh5eu5eH1+6ltNhHJBpPWyez4YIzJg0bNfXKzmN896HNSe9sIBTljy8eyFrsPRMVse1EaTnc\nFT/c3kc8DpNqSymyGRGPx4lEYzz2wn7WbGwenBpeW1nM5WdP49yFTfQHo1RXFFFdXsTO5i72Hu7B\n5/WgZtQwtaGcY10D9A1EqK0spqIsgN7fycGjvfj9Xk6fVUtTbSlHO/sZCEapry6hojRA30CY1s4B\nigJeJtWV0d49wJY97YTCMabUlVJc5Gft5hZaO/opKfKx7LRGzlVNdPQGiURjNNaUDo6FdvoQI9EY\nR9rNilKT6sogDq/sOkZnT5CKsgBnzW+grCQw6Ba593evsfH14QV80axaFs6o4Xfr9o1YqKY3lvOF\n9583zJ/Y3Rfi0bV7eXLDwcFt9dUlvG3lbBZMryYYjtFQU0JJkY8tu9s50tHPQCjC2ldbBoNrjYbP\n6+G6C2fzyLN7M7p3JtWWUldVQm1lMR09Qbbv7yAeBw8m4t/yhU0EQ1E8Hg/zp1Uza3IlbV0D9PaH\nqbYrMj29qZl9iTyfWctFS6YQjsRMnlcV0308xNObDtHc2ovf5+WMOXUsnlPHrkPdDISi7D/Sw7qt\nh0lNolNoi4t8rDxjMlecM51wJEZJsY+mmlLicTjc3ofe3zFo5Y1EZVmAb/7jxYO/I9EYj7+wn4fX\n7hl27plz6/n4O5fy5Z++nNSAgAlil25M/iVnTaW81M8fnh9fR2XA76G+qpS27oFRxSrg8zC9qYLq\n8mLOUY1Mbyxn96FuNu9uH1WUx8qMpnKuOncmP35se8Z3XFkW4MM3LGbXoW5++5fdw75UnflZ5Pdy\n3umTuOrcGUSicYqLfFSWBXh+y2HWbGwenNQ2taGcL962Iu39tuxu4xu/emVYuTkRqiuK+NzfLKeu\namgUXW9/mE9977mME6ke/fr1WVm9p4zYv+dzf4h328khJUU+JtWV0dEdpLsvlNVUZTCfP6mfzCVF\nviSf6WjHeD3G/dHVGxq8Z4k9Z7QUeD0MZnzA52FKQzk9fWE6eoKUFftorDWNRmISTKJn31ko/T4P\n1eXFdPYGJ2xBkU+9exmLZg1Fu+zqDfKfP9vA0c6RO7y8HmPZhMZpoeSC1PxMRzaf+xNBVXkRsVh8\n0OLNBg9wxTnTueHiORQFfHzzwVfZmmHM9cymCj558zK+9LOXONKe/czMmooiCY8wDjJ9bbxp+XTe\nfcWCJIMpEo3xz997btzvOd295k+v5gNvXcSk2rKk7Y+/sJ9fjhB3J1uxP2XcON2OWYADoWjS0Kds\nRS/dDLdUYRjtmFicYQGPBrL0vzmTGY7G2X9kqMe8LxgdNgY4nY88Eo0nTYqaCPT+jiSx//kTO0YV\nejDPcyoJPWQXAjhf5kvqhKZsiAN/evkg2/d3sFw1ZRR6MGuhfuehV8ck9HBqxcHxeGDhzNpRhyB6\nvZ4xd+6vWjqF1s5+tu3rHP3gLMhk9/7ppYPUVhRz9fmzBre9srNtXO/Z5/XwN29RLFdNtHb2D36x\nzZ9WPeiaTUXvP7HhmwlOGbEXcoezDnX0BNmwY2I/sYWxc7D1OMe6Rne1vH4g/eSkNwrxePLcj0yM\nZxTXhtePJY02yiWPvbCfNy2fMehLP3B0fBOlorE4sydXUVrsZ+akyowC72Si3ESjir1SygvcAywF\ngsBtWuudjv3XAZ8HIsB9Wut7RztHyC9zHKMJ9h3uGeygE04up9JXSi4JjtJZO15687iubm9/mB0H\nOgdHLZ3IYuOZJmZlYvbkSjbvHnmiWjZkk+IbgBKt9QXAp4GvJ3YopQLA3cCbgUuA25VSk0Y6R8gv\ndVXFLJlfP/i7QEc7CiksdeS5MDE4R52Nd7Hx+qriYT750Vi1dOqooRqyIRuxvwh4HEBrvQ5Y7ti3\nCNipte7QWoeAtcCqUc4R8kSR38sHrz09KV7HnClVE1JwhPwwnsbZ5/Vw69WLuP2605PiqZzKlBWf\n+h7lescImTlTKpk/QpDBTFxxzowxDxmvry7hlitPG/O9UsnmDVcBTqdbVCnl11pH0uzrAapHOUfI\nMR4PLF80ifdctZB505OXI2xshIuXTeOplw9mOFvIF6XFfvpHmRl61oJGNo5x2v0Nl8xj3ux65s2u\n59pL5tNy7Dh9AxF8Pg+PPLObZzYezGvHe3VFUdJqWum46bL5HGnv44n1ExPbZqKZObmS85ZMTRqR\nc+etK/js955NmoA1Epcsm84t15w+LmPrnW9eyOxpNfziT5odNmaQxwPnLpo8yplDZCP23YCzF8Hr\nEO3UfZVA5yjnCDnk/dcsZMm8hsEZeK2twzuSbrpoDq/v7xhX5Lx0FAW8vH3VPDweuP9Pr494rMf+\nr9C7DbIZ/nnl8uk0tx7n5Qxiftb8Bj547SK+/etIVgG1PB64cvkMrj53RlK+B4DqEjNv5ZYr5nPj\nRbNp7exn/baj/GHdyMHFnBT5vURj8TEPCb7xojls29/JC68dSbt/8dw6Vp05GZ/Xw2nTqvjzhmZ2\nNncSjox8n6baUjp7gjlvuDwe+KtVczmWJh7Nne89mzUbmlm7uYX27iDlJX7mTK1iIBRlb0s3sbjp\nkL387GksX9hEe9v4VpkCmNNUzqdvOZtjXf30DUSoqSymqmz4TNtMZCP2zwLXAb9USp0PbHbs2wYs\nUErVAb0YF85dmHKe6ZwJIV9jqU8VqsoDLJnbQHvPANv2dgx7dp/Xw/uvWcQFi0dv6StKA3zmPefw\n+Pr9PPPKoTEPISwt9rFkXgNlxX6mNpRzwRmTKLOzXTt7QxkFZNGsWj72jiW0dwf5waPDQ7+WFvkI\nRWJEY/GkOQv5YkZTOXOnVFNc5KO8NMDWPW3ssKNhspnxmWDB9Go+/s6l/Obp3fwpwxfUknn1XLty\nNl6PhzUbm/nzhoODE3km15Vx+dnTuOzsafi8Xj72jqU8+fJB1mw8SGunGZY7vbGCZQsaiMbM5LG6\nqhIuOGNyVgt4OEeCnDajhtXr9w82JuUlfhprzCTDPhu+4fTZtbxlxUwWzzH9AE9taubBNbuSfNiZ\nuG7lbC5eOpWLlk5FzajhyZcPDkZsbKot5fJl07j8nOmDHZ7nqCbOUSYm/ba97Xzz16+mnY1bWRbg\nIzedyZH2fr7/8Ja0DVBZiZ8lc+upLCuisjyA3tfB1r3mObPNz+qKIv72qoUsnpu+D6S8JMC1K2dz\n7crZo19sgmioLjX+kzEy6qQqx8iaJRiNvRU4G6jQWv/AMRrHixmN891052itt490n499fU08EfY0\nsbiuc6bmrMmVXH/hbOZbt0T38SCr1x9g/bajBMNRyor9nDmvHq/Hw5GOPrxeD1Pqy+jtC7NlTzvh\nSIyK0gBnzq0jHoejnf34vR4m15fR2Rti6552orE4VeUBptSX09rRT3tPEK/Hw8JZNTTWlHKkvY9g\n2MQGWbl4Mr39YY7aGbTFAR/rXjsyOAO0rrIYv8+bNJ69pqKI4iLf4LjpqfVlzJtWTXdfiO7jIUqL\nA3iID87sBDNz88LFk7lp1TzKSkzb3HzsOE9tbGZvSzcej4fTZtRw6VlTaRhHXJJY3EwKCvi8HDja\ny+r1+3l1VxvRWJyaiiLOmF1HKBLjWFc/xQEfZy1o5MIzJ48YymDDjlaeePEAOw50Egem1Jdx6bJp\nXLZs2mCljsfj7D7UzY6DncTjpi9h4cwaItH4YDyUY139PLXxELtbuvBg8mogGOHV3W2EwjHKiv1c\ncMYk5k2rpq17AJ/XS3VFEZt3tfGSbiUSjVFZFmDl4snMaKqko2cAv8/LvKlV7Dncw6bXjzEQitBU\nW8bFS6awaFbtsJnGA6GIsRzj8KeXD/CXV1vo6jUT/eZMqSQSNQuDxzGzgS9dNo3Lz55GwO8jHo/z\nkm7liZcOsNPGd5/WWM5ly6axaunUpBEd8Xh8MDZOeYk/bdTGxDFeD4ON60SReM6KksDgmPfe/jDF\nAV/aUA7BcJSNO1pp7RqgrNjP3CmVvLKrjW37OojG4sycVMmlZ01NO7Tw+ECYeDzzczppPnac1S/s\nZ/32I4TCMcpL/FyweDJvOW/m4CzTfYd7eHz9fl525PlFS6Zw1bkzh8WYCYaiBMNRfF54ckMzf3nl\nEG3dpp4vmVfPctVI70CEUDjK1IZylsyrP6GRN/mgsbHyjTWDtrW1Jx6Jmoh1ibGsRzr66OoNUVVe\nxOS69D3YsbiJQVHk92YsONkeE7Er+SSOCYWj+H3pY5lkIhqLEYvFB+OotHUN0NY9QFmJn2kN5Xg8\nHlKfM5VgKDq4QMXUhvLBsAv5IhYzISoCI7yvbDDPGR81psxYmag8Hw/xuIlNH/B7BwOSpeZ5OkbL\nc2Fkcpnn46nnpxJvSLE/2WkQBEF4o5Gt2IuZIQiC4AJE7AVBEFyAiL0gCIILELEXBEFwASL2giAI\nLkDEXhAEwQWI2AuCILiAU2acvSAIgpA7xLIXBEFwASL2giAILkDEXhAEwQWI2AuCILgAEXtBEAQX\nIGIvCILgAkTsBUEQXMAps6S7Y3WrpUAQuE1rvVMptQL4KnAlcB8wGygGvgj8HrgXUJhVCj+ktd6i\nlGoCXgau1FpvV0ptwKyLC7AH2AG8DSiy94wC77P7S4CzgEeBaXbfB+15PwbmYpdS1VqvUErNB/4H\nqAAagRla65hN94PAl4B1wLeBMmAOsAioB+63vx+yzxtRSn0O+KTWuloptQxYbdO5Cfg58Nf2HW0F\nOux9K4EzgN8CXwF+BswAfg38t31PAWAA+Hvgb4DLMI39X4AbgB8BlwMh4BPAB4CL7bPutNt+ad99\nCPiQPW82MAWIAO8Fnrb7vcAPgJnAFfb+B+17PMe+51JgPWZx+lX2XuuAO4E/2Oc+DlwN/Atwlc3n\nx4H32OtfBfQDNwH/bp8LYIu9zi/tffqBtwAfxZSXBrvtA8AzNv1gytUye10/sBtotmkus8+/BlNG\nrwBiwJM2n++1+fAr4AuY8uIHFgPnaq232bK5C7N858OYcuHFrP72JpunP8aUwT8A7wDqMEuA7gfe\nD/wO2GvT9BlMufiJffbXgEOYchEAVmDK0GeBHwJn2vO/bJ87jFlS9An7DJvsuzgO/BFT1rqBecAB\noBaT39hjbgYew9STfps3S+w5TfY9XwW8APTZ8zZiyv9U+36CwIvAJfY6JfYZgpg6GAeOAO/GlPFq\nTFm+0+bl32HKzks2T+7A5HMYUyaWYuq3B9iHqQM/te81ZN9hA0YTJtn7/d5eN4TJ/68DHwem2/z6\nok3PbPs7aO9/OlBjz3vEvovT7HOX2Xf8MXuPoE3DC/adJ8rlf2DqW4JyzNKu8+y9yoAmrXWbUuoW\n4KNa6wsYhVPJsr8BKLGJ/jTwdaXUP2MKaAlG5Nq01hdjKu13MOvcorW+ECMGX1JKBTAC1w+glCoB\nPFrrS7XWl2IqxUrgQkzhmqG1/h/H/pcxwhfXWq/EFJYvYQS/F/gNJjOVTfd/YYTFhym41yulvgD8\nCSP+AN/EFIQizILsd2BEqBJTiQGuU0p9w+5L5MudmMK/3abt7zCNw3b7vN/DCEKJPe7jGKGvsNct\nxhS4J7XW1ZgC/hNMhb8emIUR9HvsvwMY8fkhRmwuwRTm2cAvgKftdZ7ACFIb8I+YyjAH+DDwkta6\nxp5zK0bo/t7+rsc0tA/Y43dgxHEhRjRm2bx5GHjB3utJTKO1AlMZrwfOw1S+N9s0P4IR2hU2zddj\nKsgDNj1VGIH5hX2vH7F5OdOmcau91/XAXwHn2jRfjxGojRhBm41psCbZ9/Mue8y59l49GDHpsff6\nV6AFU5GvUEpNAV61+QKmXHwcOIoRx9uA/8SIQGIh3xvstUsxYnUO8A17zn6brq/Zd7vf7vsexjjq\nsPf+CvBvGAHahymHvwCabV79j33P3wBa7LZH7bUDmLoUwhgn04FL7TH/bs89ZN/f45jy6LHP1YJp\nLFcArVrrGnve1RgR/7D9fTOmzO1kqHxfjmlUbrbXfhFTLvZh6t+PMUbLe20eTMHUpy/Ze/dj6ubF\nGMPgEnvMHvs+m+11HseI9krgH+w7rLLHf9ae8wDwdkxZqME0wFfYY2sxxlvE7mux/z6JqZdg9Oqn\nmEb3OuB6+9ytmHJ7o01vmc2XKxzveBPwlL3XdcD/YhqHVdYY/IB936NyKon9RZgXj9Z6HbAcYwHd\nZPf/Cvic/dsDRLTWvwVut9tmYYT0LuD7GMsATKteppT6o1Lqz5jKvRljTT+KsXIAUEotx4jPdwC/\n/dqowlgIp2MK/y7gWkzlA1P5nrDp7MRYZ4ftfY7ZY24G1tpjPBiB+gKmEHiAyZiWfhVGQBJUYSy4\n05VSP8JUkJ9hCtd7MIVgF6YhOay1bsE0PN+251diKutt9vd6e59SjAXuwVhnZTa9P7XbujGFbZP9\nHbP73q+UKsKI3XHgboyF+Fl73QgQV0o9A3wLI3pFGIF4yN73PzH5+AVMQe/CVKY6hgptCUbkwFh0\nUUxj+jQmn/dgKtk7GbLQtmOs5032mAGMEF1v0zzVnvdpm+bfOK573Kb5CxjBwab5u5gG+yuYcvYF\nm4bN9tp19l5gvqS+jCl3GzDGwNswZfEAcIHdnzgGTLl4rz2miyEx/hJGNBqAv7X5uNWecw6mgZ1v\nt5fZ+2y0ab0OUy7usvmxFyMqkzBfcYcwDWGjzas/YsrzTZhGNGa3rcR8vZXb5/Ta/UeA/2vr0nx7\nzbhS6gl773MxxsYT9t8KTMNTo5TqUEodwnydVAL/oZRqx1i6HfZZNmEaw8tt+r9q7zUT06A9gylL\nb8F8OZTb8x/FNEbXYhqcMMb4iGPqzL/ZY16z7/hpzFfC1fa978TU7zKGyvIdmMZ9mX32Tvv8P8LU\n8YSOrMN8ZYbs9ocwgv08phyFGNIVBTxttSZu/6vA1IEOjE7crLXeZI+ZimnoEo3NYkz9jGPK0sfI\nklNJ7KswBT5BFNOShwG01r1a6x6lVCXGPfIvdntEKfUTjMAdxVgQqx3X6cMU/KswhfdGTIF8h/39\nc6VUQmTuxFToXowVtx0jSN/CFMJrMSKxGChSSvkwXw2/tumMAtVa6+9hBBibxhZ7zJkYYb9ba/0r\nTGFdgqnUH8QIuPMdPICp+K9hLKQmoJ2hT/o7MJXtAkyFxr6zz2EauUmABs627+1u4BV7XIV9jz8E\nYlrrNRhhvh34rHWhVWI+5YMYV1qdTcu5mMrzTZsPX8MI2nOYz8+3YsRiC0YM+zENxmqMVV2KEYAb\nMQ3FVvuO92NETgMX2nz9R3ttD0Zgv42plF6t9XOYRuFdwM+11gftOfdgKsl/YRrIY5jKdi+mUgYx\non8M0wB+EtMQrLDvYDZG/GZgKuwd9tn/BlNufm7T+COMa9GPKXsD9t1eZrclymIUYyzs0lp/y5G/\nV9l867HpPGr/227f8RyMC+Bv7DXANN6Paq3PtO/1G5i6s87mbQtG9PqABRjxudG+2w9iRKse0wg8\nYtOw1eYHDNWVu+z7+CpwPsYouhaT11dhvp7/yeZJwr16CFOG+zAW9acxDcJmmxd1mDL9FfscX7HX\nvhhjXf83pu7diKmHD9v3Msu+j26bznfYNM+1+ZWoyx/HGApTMcbh/fZai+wx/40Rx10Y43KRTfNK\nm+4rbZrrMA3OtfYdrMRY65Nten5lr7cc06D0Y8pgBLgGI+jNGFfSHvvsX8A0hh6tdRyjNT/G6MUe\nzNfJU2D0wpEXxRi9iGLcO/MY0otPYMpOVpxKYt+Nae0TeLXWEecBSqkZGH/pT7XW9ye2a63/FlMp\nPgpcpZR6CvOZ/b8Y8fyZ1jqutd6BKUzPa61DWmuNqaCNSqkaQFnR+ziwWmt9GkY0f4Kp4N0YcX0z\ncNxmQMyRRB+m9R+GUupdGOHerrVOCHMzpoI+jnErfQ8jZqXWpfMQRjCxf0cw7iEwVspyjNvhYcet\nvokpiK/Y59+NKWjNmAboJYxwrsFYxS8Cnfbdvg/jPrnf/t6EaWAutmmO2ef7NcaaXIQpoE2YhuU8\njFitwQhhKcZaud3e68s2zbdh8jqRj9/GVLqPYhqk3Rg/6nRMI3AeUOrI509h8hGMdfavwL1KqXKM\nVbfP5kWf1nqfdeN8CiM0SzEVdwemUq7UWr9sr/1mTAXvxFS+0zACsMK+56/ZbQkD4GxM5W/AiMxD\nDLkHi4ArbVmcj2lsrrG/J9t3/XfALZgGNYoRz2sw5S1krz0P0+AsxxgLbwXOsNdpwAiRB/M5fxbG\nIl5l01tqt33Glomd9rrVGLFfiilPr2O+1OqxdQVTb3oxLsA4xmXXhXFPvBPTSG/DNMTf0lovwJS9\nhDvle5ivuADGKv+yvc4PMVZpDHjE1slDNi+iwP1a6+2YRur9mLI3z/7ustd+zOZfBCOeibocwTQ0\niQb2CKZhfxYj+ndiylYH8LAjzRU2Td/ClFEv0KO1fsFet82+419orUOYBs5jr3u9/T2AaeTv1lov\nxBgVdRhjaJn99ygQTWgNptHpdPzei0UpdSumIbxMa91qj5mptZ5Osl48gPny/wajcCqJ/bOYgo5S\n6nyMNTCIUmoSplLcobW+z257r1LqM/aQPoxV82br396EsYjeivHvopSaiikEK5VSHvu7HJOZqzAF\nGUxhSFjY7ZgCex6m4F+EEZSEFbdRKXWp/bsG0xgkoZT6a4xFezOm8KGUegRjDWGf9SGb7o8C/Vrr\nj2EK7VJ7zBWYwpDogFyFsW7ehLUIHOlNCOEhjEBMZci6exFTuO+37/FqjB/5j5hP7+fsu16HEYCl\nWuvdSqnVGBG/A1PYujEV9gabttfsO3rGHtONEYMS4HF7r1UMuVL+Q2t9n71XJfBFe8whTEF+UWt9\nhU1rDDhm33OfvWbckfchjLD92L7nxKf5C0qpuxzvJQzcZ9/z39pjLlNKfdces8Le6wimLPZhLKs4\nxoJ7zG5LuPC67e92TNlbgCnHazBfDP9q79WNKbcr7e/DGIH4LqbszcS4fs7DfG18EFN+P4FpEGsw\njfRmm4efstc5Yq/zG4zVugnT8P/QHvtXdtt5GHF8u/19F0aADth3PNO+m23A/7PP9nb77P/H/q7E\niNf3GSrLxY77gfma6ccYSpdivspCmPLxU3vMjZiGeC+m8ZuKcSlFMI3dY3bbDPveuu1vvz3vMKbM\nz7T36lJKXWqPqcOUzbfYe1Vj6vs7HGlOdFrfaL/o/Zi6/Cqmbv2jTcvpSqnz7HXrMeXyXfaca+37\nusim5UWMjhwCzrfH9Nv3cwvw/+x7rsfUkw9jtOZqjF44tSehF58EfqK13m03r2bIW+DUi5uB16xe\njMgpE/XSMRpnCabVvNWOpJmNab1ewHwqbXec9nZM6zYZ+1motX7YXu8pzKfdbkyFmImptHdgBCox\nGuVOrfVqpdSngLDW+htKqQrMp+kUjIX2TYwYPoDJ1CBQqbU+RymVsPQqMZboFK111KZ7Lcaa/xKm\nkg5gPufvxmTetzACsRYzGqdFKXUR8JjWulIpdTbGQjgN08D8B6aynWXPucX++27gB1rr8+3537Dn\nvGDf06UYUerEVJgSTCH32O2/se9yAFM5y+2z9zE0SqXZpvU4RhCD9v1tt9c7HVPRbrDnhTAVIsBQ\nRY3Ye72PoREI8zHi6WfI4vssDgsLI3rvw1ixPnvujZivhxWYCv1pjKAk3Gn7MQL6Vse178BUzsmY\nRqQc01fxiD0mghG3d2AaHD9DI7dew3xleW3+fQjjPwfTgH0b01DPwQjmf2HKRcItdIXW+jUApdRe\njPX+UZvOTkye/gzjeviafZ+vAO+25WKdfc/vt/dK9CNdjWkgfogR9Vcw1uZa+wy/tWltwLhkzsAY\nCT+xvxNfozdjDJw/2/zos9tux9SdGRi330fsvSOYuvUKRvwCNu8+YN/fTPvsZTbNv7e/Q5h+ig9h\nvoB8Nj3X23e9zV77sxh3ZKIB3oFp0L5sr9mLKRN/hdEFL0MG439i3DIRm/7fYvI/atP8EsYVVWW3\nfwjT+F3GUN1Yz1B/4cuYztvfYwQ7Zp9phc1DjSlbxzEaUWXP+wim7l2F+dpcY9/7Yza9axhyx4Qx\nxuIRjF702/8OYvoX6u2z7bN5k9CL2cADWuvzGYVTRuwFQRCE3HEquXEEQRCEHCFiLwiC4AJE7AVB\nEFyAiL0gCIILELEXBEFwASL2giAILkDEXhAEwQX8f6EBCFz5f2oKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ae42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=list(range(len(pr_phred))),y=pr_phred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114e03e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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17p3fW6bm9Xi4+coGntx1nD22nVuuash3pJzIptzDQPp5QnFjjN9aG5tiWT9Q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f99400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(phred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UILovjjvnMT1zkxOpSpEu1RiYzhNzGis9uqbOXCKKEzbRlCLk+SSrdfZerrL3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\nuiZEm5UxY9AG3U8MUB6aozQ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spTTJfvvnfLTrS/xypnG+IVeu83u/s5uf/b8nefXvTvP1\nf/4A4YhJKpzki1uf4SeXXuJMweehDpenOnfx7tQx3t2fYPdrvyD9+BNomkbH136X8ulTzL3xa2LW\nbtKPrL/b8n26/+bFulXxfIZKVQaLNQaLVcYrNfxF2ZxCo38+yLt0nVb0K7eYU0px4UyG8eE88WSY\nQw8NNG6HFyy/7rpSipH6SezqYRQB2yIPsi36ILrW/B3CssHFG583AKdsUi2Z1CsmtbJO4GoMFKbZ\nP3uarZXGnO5MpJVj7buYSG8i5AXo2Qy1jglqycb8dcOJ0lvp4oWBORLhgCODvXjndH4rf5QftTzO\ntvIoqYjPud691KJx3FC+6XbfzdKlKQ7YP+Xswd9DD+sMTpfYtK2dQw8PcOLdUd585TzP/P/tnXmU\nXFd95z9vf7VXdXe1Wltbq0uWF3kFPMRggu0xDts4IQwkEEISDmcYZibMMIQMmcmBrGcgZyYzSVg8\nQMKBzAQSjAnYLGbzgg1eZNmSXFq7pZZ6r67t1dvvnT9eS2pZbS22ZMmt9znnnXpdb6l7b1d9732/\n+/v97hs3HQ1q+v7+Bxkx5rgBm0rc4urqFWzlGXY/fZjqzh3kNl+OapiseP8HGP3EHzL5d1/AHh7G\nXL7ifFf1rJKKe8oFQTeM2Nd22ddxGem6TLnHxuUqsCJnsSafYU0hgzfSwn4eM0McC57dNsHMZJdc\n3uSqI8J+CkLps6P3QybDvRhKhiuztzBgDL/g+kgBvZZBd9ai2zARcWLXVqTgUucANzafYchLkmUd\nyC3j8fIm9meWE9o++uAo2sBhFD3pjOJWP2pjObcMdbl63QxRpHDoMZXLn3oCw3P5xrKbANhY6jC5\nYgNed+n9rAvONO/btIpPPDZJt+Fxz75J7njNWsYPtti1fZKBoTxbbliNqRm8dcMb+NrurxJL6LX2\ncPuaN7J1+hkevSJH7dvfJLf5cgDMoSGGfuM3Gf/s33D403/N8Ec/hmqf3pKJLweW3rcg5YLiZ1Ot\nRd+PhGDGD5l2A6a9kHZ4LAuhpsCAbdBvGQxYJhVLR5933euGMeMyWnRiMPZjGlsnCVs+ZsWmcPVg\nYoo5xYi9HU3zVO8+XNGmrC3nqtxt2Gr+jOsqpcRpLhD0KCmzbsaU+7pc0drHlaO7KTjJ0nkHVg2y\n+7LLaPSXyE8+S6W0l3Y26dSMSKc6W6W/UWFY73HpZaOYGUk8FRB/d4L+VoRUVcaH1vBsfg1l3aPS\np0Lw8rOtny5DWYvrh/t4oHGYB/bP4CK5/S2buftLT/DTH+ylbyDH6rV9/ItV13Hvngc4EDVZ602w\nwsxy1cDlbGM7O3bso7pvH5l1yQIehVe8kt7uXbR+eD/jd32GFf/mgygXqJvomZKKe8pLgpSSdhgz\n4fpMuQENPzyqzypQtQ2qtsmAbVI29aMmltMlckJmn5ggdiMyy/OULx84Za4UKSVjwXbq7oMIYtZa\n17LefiWqcvo/bikkrWbEzExIYzYkipLoVc2IKQ+5VIpdNh/ey8YnR7C9gFhV2b1uJTtra2gWDCKt\njcMexPKkAxpoadTGLNaNeeTjEbJbJtGvKCKFJHx0jnBrh6Bcwds0gN/fz6PdKtJRuCzbeDnOmZ4x\na4cKPACUItjW6OJEMbe8dTP3/d9tfPfu7bz1166hfzDPb235Vb63429Ya+jMTm/lLetv55mZHfzk\n2jybv3U3wx/80NF7Dr79HYQT4zhbn2TmH/+B6tv+9fmr4FkkFfeUc0YkBFOuz7gbMNnz6S2I+KyY\nOlXbpGqb9FnGKdcHPRnupENz+zQykuTXlSmsL5/gojjlPMfDpTHLqL2dOWMcTRpscK+m1Kkyx/5T\nfp4qodWDmTbMOgphmHRThqFQWuZSGPAZjOdYv2uU4UcOYUQxoaFTv3wdu2uX0FB7hOIQXa0HCpih\nZPNely27XMrdJJxK3ZDDuGkAJasRtWLmtkqcYBni6g0wP7L0fMFuJ09eDRhWZuEiyIm1aiB5olqn\nmYhyjp1Nh1YQcdPtG3nsW7v41le3cee7rmVd3ypCcTlC7ubw+CNcs+pmblp5Iz8+9DAP+7vp37H9\nqHlG0XWWv/8DHPjTTzD3nfswBqqUX/f681nNs0Iq7ilnlW4YsavVY2fTYXfLIZjPN26ImNVBjxW+\nw5Dfw3oR+UzK8/lWhISpVpY5J4OiKpSvqJJdsbg5RQsCCrNT5OZm0NsTKJ0xLvVD7FDFFBYKDxMZ\nBpFhEZkWoWER2Bn8TI7AzuJaOaZEhllHY7YD4XxIq2HAsiGT/n6DUkHBPLCN9Y+MMjiZ2NM922R8\n1SDtgsVsxmE2fJpmIRHnwUbIVbtcNo56CM3Ay2SYXp+l/9oC9qBExDBzIEPjsA2mAiYYC0wGz3oV\nYlQ22w3OQ0LH88LqwTyqojA60eEjt2zkOwdneHCyyb1azPWvu4TRH47yzf+3jbe8YwvvvuFf8cTW\n//K57xwAABWcSURBVM46O+DhvQ9wx7pb+dn44zx6pWDz17/MlbVPoGjJZLmWy7Hy332Ig3/6R0x9\n+UsohknpF246z7V9caTinvKikFIy5QU823TY2XQ42PWOmlv6LIOKqbNqdA/V0D2r+aV7vs7hZp4g\n0tBzBpUtgxh5c2HBsGfGyY/sIj+6i01Th05ItysUhUhXkUqIgsR22qjy+d0lJeBoGbpGFt/Oolk6\nRt5CHfXJPNMm02mixYnqx6qCJiSuHrGvv8POdSGBoaIKhUvGBUMNG2n0M7F+kF3XFtiwfzerhmNW\nDglUVdJpWEyP2IT+4p46oVR41q9gKRHrrdaicxBLEcvUWDWYY2SigxSSO4arLM9afH1kigfViPU3\nrWTuwUPc8/dP8eZ3Xo2VuR7kYxye/D6bVl7BnZe+iS8/+zW+vabH6h//gMovHlt+zxwcZNWHPszB\nT/4Zk3/7eRRDp/jKG89jbV8cqbinnDGRkIx0XO6fbvLE+BxzfjIZqgDDeZvLyjk2lfNUbYOfT7cx\n9zx/Yq1J58DJPwtBqAoCJcb1OviKQjMu4esZRHUa3Wij9ReZ9rYj/Bgl6GE6LSyngx5GKIAyDOGG\nKq4pceyY0DDIikvIiiFMkUVlXkClJPIi3HZA2A7Qei6FqEchcqhEXfrjDvmox6A3i+rNrxo0eWKZ\nu7ZKfW2W3cMWk/3Jve0A1k7ZDDo5go5MfPRjSWF2hs3WOKuuizAM8FyY3FehPW1jWs9vZ3nW6yOQ\nGhvVGdpdEOL03TUvdNqBedLj61eWODDZZXSyw/oVJa4ZKLIsa/H3e8bZS0j5NSuJfj7J3V9+kjve\ndhMTe5/kSkvylz+/i4+86t/y5PhWdrCHH2y9hzddsQVz8FgOeWv1alb97ocZ+9SfM3HXZ4kdh8ov\n3nKuq3xOUORJRiovJdPTnQujIKdJtVpgerpz6hNfpsg4RvgeMgyRQUjHdRmd63BgrsN4u4sIArQ4\nwhYxKwyVIUNl0FAxkMlq8zJ5PdzpoR06mNxUUZOEVQu2dtgkUsGxJG1b0rQFbVvQtCWOGROq5z4d\nrRqbKEGGyM0QuTmkn0F6WWyZoWwZbOocoCBc8r02+W6LfLeFESbCKxWFbqnM1GCZfassDhc7NDNh\n0tNJKHkGy1sZ+h0Ldf7ZoeuElEuS5YOCFcsEmgZBAGMHFcbHFbJhHwCWfaKXj6aCLzS+2a4Bkteq\nezCUxdvINRfxc7/Af2XWSTJsvv13/xiAR7ZP8Nlv7uBtN6/nDa86lpfHjwV3j0zxVKODKqG4u8lg\nI+TVr21gxU9wn+PhZtbyrs2/wp/89JMEccA7d2R55Qf+4IQMkd7ICIf+518Qd9pUbr+DgTt/5aRe\nNOdTD6rVwqJGuXTkvsSRQhB3OsTtNlGnTdxuEbc7RO1W8r7TRXgewnURnnt0XwYnpqDNAZfNb4ux\nmNPjqbyGB05yLNQVAkPFMw1c3cIzLALdQpgSaYWoUmL3XDI9Pwn6keD2raC9ciPtweU01QYj+g58\ntYcmDKrharKiiCdCmr6Ph0ukeQirh2K1UTItDECNJf2tiMFGxLJGxNBMRF8rRJsXxkBXOTiU5eCQ\nza41Nq1MBIoDOAAUPJ1q12aga2HFGooisW1JIReTzwvKxQh7Pp2J68HIPo2JwxLjNA3n2/1BQjQu\nUyaeV9iXMpvX9qEAT+2dPU7cLU3l7euH2FzJ8Y3RaZqXlnHaAd7jEbdvUXmVaXNXazd3772X91z1\n63z6qS/wT+u7VL/xFdb/8ruP+wx7zRpW//7HOPQ/PsXcfd8mODTG0Ht/B61QeIlr+8JJxf0Cp/nj\nH53yHBEEiVB357dOB9HtJvuOA+I0BEDTwTQIDQu/WMExbXzDJNYNYk3DVlWKKpRUyGgKiqaTyVr4\nkUwmpTQNRdVAVUBRUBQFqSjs9TrMaj3mtC5NHBzhokmJKkATEitSyIU6BV+h4EPGB8uTqD4ofowW\nhpQ9j37pnbT4QtUIMzmsToQ5sgtl6imE7bJZU8gpVegN0g1NHN/HDiJWSYEudWw1QwUoCUk+6mJ5\nPWzfQ10wwo1VmK7oHFhuMrrcZGLAQKgKSDBjScnNUAhNBoXBoBJQMRXsisQaCrAtiWkcn9olDGFs\nXGViWmVuTkGioIn4tBa9nItsdvn95NSA1cyd+oIlSDFrsm5lkT1jLbpuSD5zfL6gK/sKrC1kuO/g\nDE/Q4cAVy7mn/TpeV3yU66TGz6a2EoqQN6+5jW+MfpcvBlv5ne+VGb71zcfdx6wOMvx7H2P8rs/g\nPL2N0Y//N4Z++31ka5teyuq+YFKzzAvkpXoMWyjuIgyJW02iZrLFrTmiZhPRW3y1HsUwUCwL1TST\nfd1AMUwUQ0cxTDq5AuPlAQ6Uqhwq9tEzjmXHy4U+65SI9bHHJbGPtcjz/Jh3EM87ZjoIiJnRPKY0\nlynNZVpzCRaMLDWpUA4tKpFNX2hTDi1sqRNFGp6fwfOyeF6GILA4kvbQtnsUMw2WtfdRGBsn4ySP\n7UKBwLIITR0vY2N5PqbnY/snD1g6GUIB17boZSzahRyNcp65coH+2TZCTY7HmkQqYBuSahEqJYVi\nSbJYYkEpwfeTbc9UmW1Ty2i4OVQR0qfOsVydTHKuz3MkkDYXVZK/n2OWERLu721gLs5wc24/Ge/5\nTRiwdM0yAPc+MspXf7SXX7v1Ul4/nxlyMfZ3XO7ZP8mkHybRwexn5OAEU+UdrCmuZrle5qdzT1Np\nRbyv7zaGX3v7CfeQQtD49j8z+42vg5QUX31TkmFywSj+QjTLpOL+AjmX/0wRhoQT4/hjY7Qfefio\nmAune8K5ajaLXi6jFUpohTxavsDB2SZSVRPPDxFDHOMZJs1cidlSH3OFCo1CmdA4NnFlhT4D3RYD\nTpsBp0Xed7GWDT1vGSWSkWg/00qPOd1jzvDoaOFxqWizsU4lyFDysxS9HFk/h4x14lgnCE3C0CQM\nDYRY+AApsG2PvNlioHeQysxh7EYLdd6l0i1kafSVaZaLuIZGC4P9y3VcaxZhOqhCYk6XsMeGsNsW\nBhE508fWPTKGj2lECF0lAISmEqsqvmngZiw820QqRzqVYxGzl+ydABK3x75+6O+HhU/nQQBOF1w3\nMbP4XiLoQQCh1Njurmc8TCbtdCIiNEDBVlw2GXspq0numIyVCH1BlhZt86e9ZewMBllrzPGq3BjN\nzsknUZeyuLecgP/0Vw8x1Jfl47/1ipOmXhZSsm2mw737x+goSepno9Ghp2wFe4wN5iDb3P1kPMGv\nBpu44c3vXdS+7u7by9SXvoh/8CBqNkfltn9J+fW3omUyqbifjItR3KUQhNNT+GNjBIcP4R8aIxgb\nI5iaPMGUoto2WrmCXi6jlyto+QKoCsJxiOZH87Hj0EVhLlugWanS7KvSrAwwV6kSWsdbvwutBgNT\nh1k2fpCh8VFKzVlQFKSmI3UdaRiQLyAtC2latPM600WYysRM2gEzlkOoHlu/SBUquV6BnFMi0y1j\ndypoQRZOcEA8rgVAjZFahC675INZ+jvj9DkNSr320bOadpEDpRU8mxtm2ijhazFxsYlWnkYtT6Go\nEikURLMKM8NklQyZXECh4JPN+agqON7xP9Yweu7aS8dzRNwtRXBVe4r+gUTQFSUZkbfb0GhAuwXe\n81iMWnGOJ3ubcEWGktZms72Pktal45uMRisZi5NEVWv1Awxrh8jazy/uo0GZR73V5FWf2/J7sdT4\nohZ3gM/es51Hdkzy/rdczisuW3bKe8dC8NCO7/O4W2SaJMun4gfE/gh5dReT8WEkcN20zZ03vZfS\nqhPXWZVxTPP+7zP7z/cgeg5qLkfpptey9q130NXPPGXF2SAV97PM6Yq7lJK42yGcnCScmiKYmkhe\nJyYIJsZPmLhUMxnMlauwVq7CWrkSf2ICVBXR6xG1mvS6Dq1Y0tEMOoUS3UKZTqFMt1imWygTmsfb\nBxQhyPc6lLptSp0m/a1Z+pqzWKGPEscoUYQSRwRhSCQNfNWkkdOZKak0ygrNUkSnEBDrx4uh6ebI\ndstku2UyTgW7l0c5YjQWEcgYOb8JKVBkhCJCNBGixT65sE3Zn6UcdilGDqY8NlKOUDmUGWRPfhWz\ngwPkBwOGKk2qeYe8FZLTBZaiIGUitIFUcSMDX2o4QqUT63RjjU6sE0gFUPC8051ekuS1mLVFhxWm\nT9UIjgYItdvQmE1EPTyJ9UdKOBguY6e7HoHKOusgG60DqPP5yI8sjtESeXYENXws+tQGV+d2Y6kn\n3vhwMMA291I0BDfmnyKvucfdZylxJuI+Ndfjv3zuUfJZgz98zw2U8qdedEPEAVN7v8JYt8uT7mZG\n1JVIPWnH2B/D935MqPawfbgxWMbrr/9lKivXnHCf2HVp3v895r73HYSTTKRnNl5K/prryF19zXHu\nleeaFyzutVpNBf4a2EIS4Pzb9Xp9z4LjbwL+KxABn6/X65871TWL8XIU96mpNsJ1iZqJ7Tuamzu2\n35wjajQIpyYR7olfWEXXMZevQFu5EgaHCAolXCtDV0i6nS4dx6UbRjiqhpvN08vmcbN5Yn3xxSZ0\nKSiIkLwMEK1pjLCNFrYg6BEKhUhoRFIjijWE0IiRxArEWkxo+ISWi293CS33+MG2BNPLYffy2L0s\nua5JoaWR80IyYY9s6JALHfKhgxF7GLGPegbDw0DTcQ0Lpz9LMJQlHtQxKwrlrKBiyhNWSQqlxIsh\nisCQiR1fWioZVSyaW8UXCt1YpxkYtCOdTpgIv5DJyboqyWkxOS2izwypmgG5BR3ZbGjQPRTSaCRm\nllPRjTM8661lOurDUEKuyuxi0Dh+4nOhKAdSZ2e4kTlRQSNinXWI5cY0lhrQjbOMBis4HA6iEXFd\nbgf9envR+ywVzkTcAe579AD/8MM9rKzm+OCdVzJYyZ7yM0QcMDPyNbz2HgJp8/DEZRwUK/FKWSJL\n4gdP4wdPASFIlVLUx1pzFVetvIJLV9co2iba/JdNhAHdxx+j98hDtLfvSHp2QO/rI7PhUqzhYcyh\n5ZhDyzGq1aMRsWeTFyPudwJvrtfr76nVaq8CPlqv198yf8wAdgI3kPiBPQS8EXj1813zfLxQcQ8b\nDfzREdBUFFU75rmhaYndTFFBCpwgJIgFMhZIKZLZKXlkX0AUIf0AGYYQBoj5VxkESM9D9HpIt4fs\n9ZA9B3oOeB7KSTxRhKbhFUr0iiWcQjKybuXLNIsVWsUKgWGedKFiKX3ieAakRI9iVBEnNnQRoYQ+\nhD5EPgQhxAJVqiCVxMVakUhFIrSIWA+JtZDQ9IhMD6EtbpJQIx0tMlCEhioVhCIRWkhsBEj15P8e\nRUgyviTvCzYZOmoESihRYokmFRRVQeoKiq5gmCqaraDbKlldpV9TsZ/jBhhJyXQsaISStguOoxI3\nFOyOiiaTNjOMpB6j64eSCFNVkNciClpMQYvIz7/mtBjtNMPz3Vhl2jeZjk0OBRae0I7a3J+LL3R6\nIoMvTbpxlpmozFycmFT6tCZXZXeTUU8MRHquKEsJh+MhRuLVhPLEzruodrkqu4uCdvzEeSruyZPx\nV76/m/sfH0PXFK5c18+V6/qpVjL0F22G+hYXeykl3Zmf0xr/ESJOoqobTpG9c/3s6qwlLNh0Cvvx\n2YsQjQVX6qhqEZ0CpshhCZOMVqZaWo/ldBk6uJ+Bg/vJHBpB7TnHf6iqogxUUYsljGIRq1REyxcw\nBwcpvOJVL1j4X4yf+y8A9wHU6/VHarXa9QuOXQbsqdfrcwC1Wu1B4DXAjSe55qwy+YX/Q2/n9nN1\n+xOIVZXAyhBYGfx8Bd/O0MsV6GXzyeuRLVvAtzMnLG+mxAIllqihwHBD1FCgRnL+VSSv89tU9VGc\n0uETC6EC1vx2JoQGip9BDWy00MQMDTKRSSk0yfiCohRk1YisGqIqgliVxIokUiWRmkSKhgv2m7jJ\nMY3kXE2yrKBw48DpF0xISSuCcVfSCcBxVVxHJ+5qaEHSMQAYgH0SW4hEwRUartCYfs5pChItVCga\nEUU9JKfFR/8tkVToRRpOrNEKDbrx/GTnggnVRT9Pwk861xMd9xOS9GlN1liHGdRPP0ujosBKfYJL\nspMcDqrMxSVCqZFRAqpG44zudbGhKArvvGUjG1eV+OZDIzy5e4Yndx9LEvfhd1zDZZdUFr2uUH0F\nub4tOI2n6DV30K8coj/XZpm8lm37Y8SuEna0CSMXEuUdvGybwHCIZJtAaRAAXRWQ0I6GUewiOzdu\ngY1bEq+aVoPy3DSl5iylZoNSc4ZCaw57apIYWDhVY60exlq1+qy2zemIe5Hj41PiWq2m1+v1aJFj\nHaB0imsW5fl6n1NR/bOPv5DLXibcdr4LkHISPnC+C5BylF8aLPJLr9nwAq4swNDrgWNZIK8D7jxb\nBTuPnM5zXRtYGJalLhDp5x4rAM1TXJOSkpKSco45HXF/CLgDYN5+/vSCYzuBjbVara9Wq5kkJpmf\nnuKalJSUlJRzzJl4y1xF4kfxm8C1QL5er392gbeMSuIt81eLXVOv1589d9VISUlJSVnIBePnnpKS\nkpJy9lh6vlQpKSkpKam4p6SkpCxF0pS/Z8ALibxdStRqtVcCf16v12+u1WobgC+SZCt5BvhAvV5f\nssnF5wP2Pg+sIYkw+CNgBxdXG2jA54BkpRB4P4m79he5SNrgCLVabRB4HLiVJDr/i1xgbZCO3M+M\ntwJ2vV6/Efg94FPnuTwvGbVa7T8Dd3Fs/Y2/AD5Wr9dvIpk0P2kE8hLg14HZ+freDvxvLr42eBNA\nvV5/NfAx4I+5+NrgSEf/GeBIOO0F2QapuJ8Zx0XrAucs8vYCZC/Hx3ZcB/x4fv9e4OW50OTp81Xg\nD+b3FZLR2kXVBvV6/W7gffN/XkIS03JRtcE8nwQ+DRwJH78g2yAV9zNj0cjb81WYl5J6vf6PwMLA\nfqVerx9xtToSmbxkqdfr3Xq93qnVagXgayQj14uqDQDq9XpUq9X+FvhfwJe5yNqgVqu9B5iu1+vf\nWfD2BdkGqbifGWnk7TEW2hSPRCYvaWq12mrgh8CX6vX6V7gI2wCgXq//BnApif09s+DQxdAG7wVu\nrdVqPwKuBv4OWJjf94Jpg1Tcz4w08vYYT9ZqtZvn998APHAey3LOqdVqy4DvAh+p1+ufn3/7YmuD\nd9VqtY/O/9kj6dweu5jaoF6vv6Zer7+2Xq/fDGwF3g3ceyG2wUVhUjiLfJ2k136YY9G6Fyv/Efjc\nfNqJnSSmiqXM7wMV4A9qtdoR2/u/B/7yImqDfwK+UKvVfkKSqPM/kNT7YvoeLMYF+VtII1RTUlJS\nliCpWSYlJSVlCZKKe0pKSsoSJBX3lJSUlCVIKu4pKSkpS5BU3FNSUlKWIKm4p6SkpCxBUnFPSUlJ\nWYKk4p6SkpKyBPn/LdsOEq41wNEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115244710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "count = 0\n",
    "for record in SeqIO.parse(\"untreated1_chr4.fq\", \"fastq\"):\n",
    "    sns.distplot(record.letter_annotations[\"phred_quality\"])\n",
    "    count += 1\n",
    "    if count > 8:\n",
    "       break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(phred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
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